| ▲ | asib an hour ago | |
Does anyone have any understanding of how they do this? My knowledge of how these models work is basically that they are a black box that you put text into and get text out of. I don't phrase it this way to diminish their capability, but more to ask how, other than using a technique like stenography, are they able to hide their true chain of thought in a recoverable way? | ||
| ▲ | rhdunn 40 minutes ago | parent [-] | |
Welch Labs on YouTube has a great collection of videos on how AI models learn. His recent video [1] covers how image models can learn to encode reasoning in the image processing layers when not given an out of band reasoning set of weights to use instead. I suspect that this applies to LLMs and CoT reasoning vs output token weights. | ||